/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    AprioriItemSet.java
 *    Copyright (C) 2004-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.associations;

import java.io.Serializable;
import java.util.ArrayList;
import java.util.Enumeration;
import java.util.Hashtable;

import weka.core.ContingencyTables;
import weka.core.Instances;
import weka.core.WekaEnumeration;

/**
 * Class for storing a set of items. Item sets are stored in a lexicographic
 * order, which is determined by the header information of the set of instances
 * used for generating the set of items. All methods in this class assume that
 * item sets are stored in lexicographic order. The class provides methods that
 * are used in the Apriori algorithm to construct association rules.
 * 
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @author Stefan Mutter (mutter@cs.waikato.ac.nz)
 * @version $Revision$
 */
public class AprioriItemSet extends ItemSet implements Serializable {

    /** for serialization */
    static final long serialVersionUID = 7684467755712672058L;

    /**
     * Constructor
     * 
     * @param totalTrans the total number of transactions in the data
     */
    public AprioriItemSet(int totalTrans) {
        super(totalTrans);
    }

    /**
     * Outputs the confidence for a rule.
     * 
     * @param premise     the premise of the rule
     * @param consequence the consequence of the rule
     * @return the confidence on the training data
     */
    public static double confidenceForRule(AprioriItemSet premise, AprioriItemSet consequence) {

        return (double) consequence.m_counter / (double) premise.m_counter;
    }

    /**
     * Outputs the lift for a rule. Lift is defined as:<br>
     * confidence / prob(consequence)
     * 
     * @param premise          the premise of the rule
     * @param consequence      the consequence of the rule
     * @param consequenceCount how many times the consequence occurs independent of
     *                         the premise
     * @return the lift on the training data
     */
    public double liftForRule(AprioriItemSet premise, AprioriItemSet consequence, int consequenceCount) {
        double confidence = confidenceForRule(premise, consequence);

        return confidence / ((double) consequenceCount / (double) m_totalTransactions);
    }

    /**
     * Outputs the leverage for a rule. Leverage is defined as: <br>
     * prob(premise & consequence) - (prob(premise) * prob(consequence))
     * 
     * @param premise          the premise of the rule
     * @param consequence      the consequence of the rule
     * @param premiseCount     how many times the premise occurs independent of the
     *                         consequent
     * @param consequenceCount how many times the consequence occurs independent of
     *                         the premise
     * @return the leverage on the training data
     */
    public double leverageForRule(AprioriItemSet premise, AprioriItemSet consequence, int premiseCount, int consequenceCount) {
        double coverageForItemSet = (double) consequence.m_counter / (double) m_totalTransactions;
        double expectedCoverageIfIndependent = ((double) premiseCount / (double) m_totalTransactions) * ((double) consequenceCount / (double) m_totalTransactions);
        double lev = coverageForItemSet - expectedCoverageIfIndependent;
        return lev;
    }

    /**
     * Outputs the conviction for a rule. Conviction is defined as: <br>
     * prob(premise) * prob(!consequence) / prob(premise & !consequence)
     * 
     * @param premise          the premise of the rule
     * @param consequence      the consequence of the rule
     * @param premiseCount     how many times the premise occurs independent of the
     *                         consequent
     * @param consequenceCount how many times the consequence occurs independent of
     *                         the premise
     * @return the conviction on the training data
     */
    public double convictionForRule(AprioriItemSet premise, AprioriItemSet consequence, int premiseCount, int consequenceCount) {
        double num = (double) premiseCount * (double) (m_totalTransactions - consequenceCount) / m_totalTransactions;
        double denom = ((premiseCount - consequence.m_counter) + 1);

        if (num < 0 || denom < 0) {
            System.err.println("*** " + num + " " + denom);
            System.err.println("premis count: " + premiseCount + " consequence count " + consequenceCount + " total trans " + m_totalTransactions);
        }
        return num / denom;
    }

    /**
     * Generates all rules for an item set.
     * 
     * @param minConfidence the minimum confidence the rules have to have
     * @param hashtables    containing all(!) previously generated item sets
     * @param numItemsInSet the size of the item set for which the rules are to be
     *                      generated
     * @return all the rules with minimum confidence for the given item set
     */
    public ArrayList<Object>[] generateRules(double minConfidence, ArrayList<Hashtable<ItemSet, Integer>> hashtables, int numItemsInSet) {

        ArrayList<Object> premises = new ArrayList<Object>(), consequences = new ArrayList<Object>(), conf = new ArrayList<Object>();
        // TODO
        ArrayList<Object> lift = new ArrayList<Object>(), lev = new ArrayList<Object>(), conv = new ArrayList<Object>();
        // TODO
        @SuppressWarnings("unchecked")
        ArrayList<Object>[] rules = new ArrayList[6], moreResults;
        AprioriItemSet premise, consequence;
        Hashtable<ItemSet, Integer> hashtable = hashtables.get(numItemsInSet - 2);

        // Generate all rules with one item in the consequence.
        for (int i = 0; i < m_items.length; i++) {
            if (m_items[i] != -1) {
                premise = new AprioriItemSet(m_totalTransactions);
                consequence = new AprioriItemSet(m_totalTransactions);
                premise.m_items = new int[m_items.length];
                consequence.m_items = new int[m_items.length];
                consequence.m_counter = m_counter;

                for (int j = 0; j < m_items.length; j++) {
                    consequence.m_items[j] = -1;
                }
                System.arraycopy(m_items, 0, premise.m_items, 0, m_items.length);
                premise.m_items[i] = -1;

                consequence.m_items[i] = m_items[i];
                premise.m_counter = hashtable.get(premise).intValue();

                Hashtable<ItemSet, Integer> hashtableForConsequence = hashtables.get(0);
                int consequenceUnconditionedCounter = hashtableForConsequence.get(consequence).intValue();
                consequence.m_secondaryCounter = consequenceUnconditionedCounter;

                premises.add(premise);
                consequences.add(consequence);
                conf.add(new Double(confidenceForRule(premise, consequence)));

                double tempLift = liftForRule(premise, consequence, consequenceUnconditionedCounter);
                double tempLev = leverageForRule(premise, consequence, premise.m_counter, consequenceUnconditionedCounter);
                double tempConv = convictionForRule(premise, consequence, premise.m_counter, consequenceUnconditionedCounter);
                lift.add(new Double(tempLift));
                lev.add(new Double(tempLev));
                conv.add(new Double(tempConv));
            }
        }
        rules[0] = premises;
        rules[1] = consequences;
        rules[2] = conf;

        rules[3] = lift;
        rules[4] = lev;
        rules[5] = conv;

        pruneRules(rules, minConfidence);

        // Generate all the other rules
        moreResults = moreComplexRules(rules, numItemsInSet, 1, minConfidence, hashtables);
        if (moreResults != null) {
            for (int i = 0; i < moreResults[0].size(); i++) {
                rules[0].add(moreResults[0].get(i));
                rules[1].add(moreResults[1].get(i));
                rules[2].add(moreResults[2].get(i));

                // TODO
                rules[3].add(moreResults[3].get(i));
                rules[4].add(moreResults[4].get(i));
                rules[5].add(moreResults[5].get(i));
            }
        }
        return rules;
    }

    /**
     * Generates all significant rules for an item set.
     * 
     * @param minMetric         the minimum metric (confidence, lift, leverage,
     *                          improvement) the rules have to have
     * @param metricType        (confidence=0, lift, leverage, improvement)
     * @param hashtables        containing all(!) previously generated item sets
     * @param numItemsInSet     the size of the item set for which the rules are to
     *                          be generated
     * @param numTransactions
     * @param significanceLevel the significance level for testing the rules
     * @return all the rules with minimum metric for the given item set
     * @exception Exception if something goes wrong
     */
    public final ArrayList<Object>[] generateRulesBruteForce(double minMetric, int metricType, ArrayList<Hashtable<ItemSet, Integer>> hashtables, int numItemsInSet, int numTransactions, double significanceLevel) throws Exception {

        ArrayList<Object> premises = new ArrayList<Object>(), consequences = new ArrayList<Object>(), conf = new ArrayList<Object>(), lift = new ArrayList<Object>(), lev = new ArrayList<Object>(), conv = new ArrayList<Object>();
        @SuppressWarnings("unchecked")
        ArrayList<Object>[] rules = new ArrayList[6];
        AprioriItemSet premise, consequence;
        Hashtable<ItemSet, Integer> hashtableForPremise, hashtableForConsequence;
        int numItemsInPremise, help, max, consequenceUnconditionedCounter;
        double[][] contingencyTable = new double[2][2];
        double metric, chiSquared = 0;

        // Generate all possible rules for this item set and test their
        // significance.
        max = (int) Math.pow(2, numItemsInSet);
        for (int j = 1; j < max; j++) {
            numItemsInPremise = 0;
            help = j;
            while (help > 0) {
                if (help % 2 == 1) {
                    numItemsInPremise++;
                }
                help /= 2;
            }
            if (numItemsInPremise < numItemsInSet) {
                hashtableForPremise = hashtables.get(numItemsInPremise - 1);
                hashtableForConsequence = hashtables.get(numItemsInSet - numItemsInPremise - 1);
                premise = new AprioriItemSet(m_totalTransactions);
                consequence = new AprioriItemSet(m_totalTransactions);
                premise.m_items = new int[m_items.length];

                consequence.m_items = new int[m_items.length];
                consequence.m_counter = m_counter;
                help = j;
                for (int i = 0; i < m_items.length; i++) {
                    if (m_items[i] != -1) {
                        if (help % 2 == 1) {
                            premise.m_items[i] = m_items[i];
                            consequence.m_items[i] = -1;
                        } else {
                            premise.m_items[i] = -1;
                            consequence.m_items[i] = m_items[i];
                        }
                        help /= 2;
                    } else {
                        premise.m_items[i] = -1;
                        consequence.m_items[i] = -1;
                    }
                }
                premise.m_counter = hashtableForPremise.get(premise).intValue();
                consequenceUnconditionedCounter = hashtableForConsequence.get(consequence).intValue();
                consequence.m_secondaryCounter = consequenceUnconditionedCounter;

                if (significanceLevel != -1) {
                    contingencyTable[0][0] = (consequence.m_counter);
                    contingencyTable[0][1] = (premise.m_counter - consequence.m_counter);
                    contingencyTable[1][0] = (consequenceUnconditionedCounter - consequence.m_counter);
                    contingencyTable[1][1] = (numTransactions - premise.m_counter - consequenceUnconditionedCounter + consequence.m_counter);
                    chiSquared = ContingencyTables.chiSquared(contingencyTable, false);
                }

                if (metricType == 0) {

                    metric = confidenceForRule(premise, consequence);

                    if ((!(metric < minMetric)) && (significanceLevel == -1 || !(chiSquared > significanceLevel))) {
                        premises.add(premise);
                        consequences.add(consequence);
                        conf.add(new Double(metric));
                        lift.add(new Double(liftForRule(premise, consequence, consequenceUnconditionedCounter)));
                        lev.add(new Double(leverageForRule(premise, consequence, premise.m_counter, consequenceUnconditionedCounter)));
                        conv.add(new Double(convictionForRule(premise, consequence, premise.m_counter, consequenceUnconditionedCounter)));
                    }
                } else {
                    double tempConf = confidenceForRule(premise, consequence);
                    double tempLift = liftForRule(premise, consequence, consequenceUnconditionedCounter);
                    double tempLev = leverageForRule(premise, consequence, premise.m_counter, consequenceUnconditionedCounter);
                    double tempConv = convictionForRule(premise, consequence, premise.m_counter, consequenceUnconditionedCounter);
                    switch (metricType) {
                    case 1:
                        metric = tempLift;
                        break;
                    case 2:
                        metric = tempLev;
                        break;
                    case 3:
                        metric = tempConv;
                        break;
                    default:
                        throw new Exception("ItemSet: Unknown metric type!");
                    }
                    if (!(metric < minMetric) && (significanceLevel == -1 || !(chiSquared > significanceLevel))) {
                        premises.add(premise);
                        consequences.add(consequence);
                        conf.add(new Double(tempConf));
                        lift.add(new Double(tempLift));
                        lev.add(new Double(tempLev));
                        conv.add(new Double(tempConv));
                    }
                }
            }
        }
        rules[0] = premises;
        rules[1] = consequences;
        rules[2] = conf;
        rules[3] = lift;
        rules[4] = lev;
        rules[5] = conv;
        return rules;
    }

    /**
     * Subtracts an item set from another one.
     * 
     * @param toSubtract the item set to be subtracted from this one.
     * @return an item set that only contains items form this item sets that are not
     *         contained by toSubtract
     */
    public final AprioriItemSet subtract(AprioriItemSet toSubtract) {

        AprioriItemSet result = new AprioriItemSet(m_totalTransactions);

        result.m_items = new int[m_items.length];

        for (int i = 0; i < m_items.length; i++) {
            if (toSubtract.m_items[i] == -1) {
                result.m_items[i] = m_items[i];
            } else {
                result.m_items[i] = -1;
            }
        }
        result.m_counter = 0;
        return result;
    }

    /**
     * Generates rules with more than one item in the consequence.
     * 
     * @param rules                 all the rules having (k-1)-item sets as
     *                              consequences
     * @param numItemsInSet         the size of the item set for which the rules are
     *                              to be generated
     * @param numItemsInConsequence the value of (k-1)
     * @param minConfidence         the minimum confidence a rule has to have
     * @param hashtables            the hashtables containing all(!) previously
     *                              generated item sets
     * @return all the rules having (k)-item sets as consequences
     */
    @SuppressWarnings("unchecked")
    private final ArrayList<Object>[] moreComplexRules(ArrayList<Object>[] rules, int numItemsInSet, int numItemsInConsequence, double minConfidence, ArrayList<Hashtable<ItemSet, Integer>> hashtables) {

        AprioriItemSet newPremise;
        ArrayList<Object>[] result, moreResults;
        ArrayList<Object> newConsequences, newPremises = new ArrayList<Object>(), newConf = new ArrayList<Object>();
        Hashtable<ItemSet, Integer> hashtable;

        ArrayList<Object> newLift = null, newLev = null, newConv = null;
        // if (rules.length > 3) {
        newLift = new ArrayList<Object>();
        newLev = new ArrayList<Object>();
        newConv = new ArrayList<Object>();
        // }

        if (numItemsInSet > numItemsInConsequence + 1) {
            hashtable = hashtables.get(numItemsInSet - numItemsInConsequence - 2);
            newConsequences = mergeAllItemSets(rules[1], numItemsInConsequence - 1, m_totalTransactions);
            int newNumInConsequence = numItemsInConsequence + 1;

            Hashtable<ItemSet, Integer> hashtableForConsequence = hashtables.get(newNumInConsequence - 1);

            Enumeration<Object> enu = new WekaEnumeration<Object>(newConsequences);
            while (enu.hasMoreElements()) {
                AprioriItemSet current = (AprioriItemSet) enu.nextElement();
                for (int m_item : current.m_items) {
                    if (m_item != -1) {
                    }
                }

                current.m_counter = m_counter;
                newPremise = subtract(current);
                newPremise.m_counter = hashtable.get(newPremise).intValue();
                newPremises.add(newPremise);
                newConf.add(new Double(confidenceForRule(newPremise, current)));

                // if (rules.length > 3) {
                int consequenceUnconditionedCounter = hashtableForConsequence.get(current).intValue();
                current.m_secondaryCounter = consequenceUnconditionedCounter;

                double tempLift = liftForRule(newPremise, current, consequenceUnconditionedCounter);
                double tempLev = leverageForRule(newPremise, current, newPremise.m_counter, consequenceUnconditionedCounter);
                double tempConv = convictionForRule(newPremise, current, newPremise.m_counter, consequenceUnconditionedCounter);

                newLift.add(new Double(tempLift));
                newLev.add(new Double(tempLev));
                newConv.add(new Double(tempConv));
                // }
            }
            result = new ArrayList[rules.length];
            result[0] = newPremises;
            result[1] = newConsequences;
            result[2] = newConf;

            // if (rules.length > 3) {
            result[3] = newLift;
            result[4] = newLev;
            result[5] = newConv;
            // }
            pruneRules(result, minConfidence);
            moreResults = moreComplexRules(result, numItemsInSet, numItemsInConsequence + 1, minConfidence, hashtables);
            if (moreResults != null) {
                for (int i = 0; i < moreResults[0].size(); i++) {
                    result[0].add(moreResults[0].get(i));
                    result[1].add(moreResults[1].get(i));
                    result[2].add(moreResults[2].get(i));
                    //
                    result[3].add(moreResults[3].get(i));
                    result[4].add(moreResults[4].get(i));
                    result[5].add(moreResults[5].get(i));
                }
            }
            return result;
        } else {
            return null;
        }
    }

    /**
     * Returns the contents of an item set as a string.
     * 
     * @param instances contains the relevant header information
     * @return string describing the item set
     */
    @Override
    public final String toString(Instances instances) {

        return super.toString(instances);
    }

    /**
     * Converts the header info of the given set of instances into a set of item
     * sets (singletons). The ordering of values in the header file determines the
     * lexicographic order.
     * 
     * @param instances the set of instances whose header info is to be used
     * @return a set of item sets, each containing a single item
     * @exception Exception if singletons can't be generated successfully
     */
    public static ArrayList<Object> singletons(Instances instances, boolean treatZeroAsMissing) throws Exception {

        ArrayList<Object> setOfItemSets = new ArrayList<Object>();
        AprioriItemSet current;

        for (int i = 0; i < instances.numAttributes(); i++) {
            if (instances.attribute(i).isNumeric()) {
                throw new Exception("Can't handle numeric attributes!");
            }
            int j = (treatZeroAsMissing) ? 1 : 0;
            for (; j < instances.attribute(i).numValues(); j++) {
                current = new AprioriItemSet(instances.numInstances());
                current.m_items = new int[instances.numAttributes()];
                for (int k = 0; k < instances.numAttributes(); k++) {
                    current.m_items[k] = -1;
                }
                current.m_items[i] = j;
                setOfItemSets.add(current);
            }
        }
        return setOfItemSets;
    }

    /**
     * Merges all item sets in the set of (k-1)-item sets to create the (k)-item
     * sets and updates the counters.
     * 
     * @param itemSets   the set of (k-1)-item sets
     * @param size       the value of (k-1)
     * @param totalTrans the total number of transactions in the data
     * @return the generated (k)-item sets
     */
    public static ArrayList<Object> mergeAllItemSets(ArrayList<Object> itemSets, int size, int totalTrans) {

        ArrayList<Object> newVector = new ArrayList<Object>();
        AprioriItemSet result;
        int numFound, k;

        for (int i = 0; i < itemSets.size(); i++) {
            ItemSet first = (ItemSet) itemSets.get(i);
            out: for (int j = i + 1; j < itemSets.size(); j++) {
                ItemSet second = (ItemSet) itemSets.get(j);
                result = new AprioriItemSet(totalTrans);
                result.m_items = new int[first.m_items.length];

                // Find and copy common prefix of size 'size'
                numFound = 0;
                k = 0;
                while (numFound < size) {
                    if (first.m_items[k] == second.m_items[k]) {
                        if (first.m_items[k] != -1) {
                            numFound++;
                        }
                        result.m_items[k] = first.m_items[k];
                    } else {
                        break out;
                    }
                    k++;
                }

                // Check difference
                while (k < first.m_items.length) {
                    if ((first.m_items[k] != -1) && (second.m_items[k] != -1)) {
                        break;
                    } else {
                        if (first.m_items[k] != -1) {
                            result.m_items[k] = first.m_items[k];
                        } else {
                            result.m_items[k] = second.m_items[k];
                        }
                    }
                    k++;
                }
                if (k == first.m_items.length) {
                    result.m_counter = 0;
                    newVector.add(result);
                }
            }
        }
        return newVector;
    }

}
